Secondly, CasADi is not a computer algebra system. If you have an existing model written in C++, Python or MATLAB/Octave, you need to be prepared to reimplement the model using CasADi syntax. It is important to point out that CasADi is not a conventional AD tool, that can be used to calculate derivative information from existing user code with little to no modification. In its current form, it is a general-purpose tool for gradient-based numerical optimization – with a strong focus on optimal control – and CasADi is just a name without any particular meaning. While AD still forms one of the core functionalities of the tool, the scope of the tool has since been considerably broadened, with the addition of support for ODE/DAE integration and sensitivity analysis, nonlinear programming and interfaces to other numerical tools. What CasADi is and what it is not ¶ĬasADi started out as a tool for algorithmic differentiation (AD) using a syntax borrowed from computer algebra systems (CAS), which explains its name. The MATLAB module has been tested successfully for Octave (version 4.0.2 or later). The C++ API is stable, but is not ideal for getting started with CasADi since there is limited documentation and since it lacks the interactivity of interpreted languages like MATLAB and Python.
In general, the Python API is the best documented and is slightly more stable than the MATLAB API. After reading it, you should be able to formulate and manipulate expressions in CasADi’s symbolic framework, generate derivative information efficiently using algorithmic differentiation, to set up, solve and perform forward and adjoint sensitivity analysis for systems of ordinary differential equations (ODE) or differential-algebraic equations (DAE) as well as to formulate and solve nonlinear programs (NLP) problems and optimal control problems (OCP).ĬasADi is available for C++, Python and MATLAB/Octave with little or no difference in performance. This document aims at giving a condensed introduction to CasADi. (OPTEC) of the KU Leuven under supervision of Moritz Diehl. Joel Andersson and Joris Gillis while PhD students at the Optimization in Engineering Center optimization involving differential equations) in particular. Difference in usage from different languagesĬasADi is an open-source software tool for numerical optimization in general and optimal control Derivative calculation using finite differences Initial-value problems and sensitivity analysis
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